On Genome-Wide Association Studies and Their Meta-Analyses: Lessons Learned From Osteoporosis Studies

Abstract

Genome-wide association studies (GWASs) and meta-analyses of GWASs have led to the identification of a number of promising genes for osteoporosis. However, inconsistent findings are seen among and between GWASs and meta-analyses, and inconsistencies have even been observed between meta-analyses whose samples overlapped to a large extent. We carefully evaluated the usefulness and limitations of GWASs and their meta-analyses, with an emphasis on understanding the reasons for inconsistent results. Based on published empirical data for osteoporosis, we performed a series of theoretical analyses using simulation studies. The power of meta-analyses is limited to identifying a particular locus with modest effect size. In the situation in which individual GWASs were not included in the meta-analysis (ie, nonoverlap), the meta-analysis has rather limited power to replicate particular loci identified from the individual GWASs. Between-study heterogeneity may result in a power loss in meta-analyses, implying that adding heterogeneous samples into a meta-analysis may reduce the power, rather than having the anticipated effect of increasing power due to increased sample size. Discordant findings in GWASs and meta-analyses are not unexpected, even for true susceptible genes. Contrary to the general belief, meta-analyses should not and cannot be used as a gold standard to evaluate the results of individual GWASs. Individual GWASs in homogeneous populations can detect true disease genes that meta-analyses may have low power to replicate.